title: An accurate model for prediction of autoignition temperature of pure compounds.
abstract: Accurate prediction of pure compounds autoignition temperature (AIT) is of great importance. In this study, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to evaluate the AIT of pure compounds. 1025 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient of 0.984, root mean square error of 15.44K, and average percent error of 1.6% for the experimental values.
authors.affiliation: Saman Energy Giti Co, Postal Code 3331619636, Tehran, Iran. fghara@ut.ac.ir
authors.forename: Farhad
authors.initials: F
authors.lastname: Gharagheizi
mesh_terms: D055598:Chemical Phenomena; D005390:Fires; D008956:Models, Chemical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D013696:Temperature